Risk Assessment

This week we talk about patients’ prognosis. Not all patients are the same.
They may have the same disease but radically different severity of illness. All
analysis of healthcare outcomes starts with understanding the patient and their
comorbidities better. This week we answer age old patient questions such as
“What is going to happen to me?” A few years ago, I designed the multimorbidity
indices. These indices are quite accurate and a good way to capture differences
among the patients. We will discuss how CMS currently measures patients’
prognosis. The information you learn this week will be used in subsequent weeks
to do risk-adjusted control charts. This week we focus on the use of these
indices in measuring patients’ prognoses. .

Calculation of sensitivity, specificity, and Area under the Receiver Operating Curves
Excel►Slides►YouTube►

Assignments

Instruction for Submission of Assignments: Assignments
should be submitted directly on Blackboard. In rare situations
assignments can be sent directly by email to the instructor. Submission
should follow these rules:

Submit only one document, usually an Excel file. All
questions should be answered in different sheets. Each sheet
should be labeled with the question number.

All Excel cells, except the cells containing the data, must have
formulas. Do not paste the value into the cell, it must be
calculated using a formula. Even simple steps, such as adding two
numbers, should be done using formulas.

Make sure that any control charts follow the visual rules below:
(1) Control limits must be in red and without markers, (2) Observed
lines must have markers, (3) X and Y axis must be labeled, and (4)
Charts must be linked to the data.

Copy and paste SQL or R code into Excel sheet. Plot data in
Excel.

The first sheet in the file should be a summary page. In the
summary page you should list how your answers to the question differs
from answers provided within the assignment (inside Teach One or other
answers). You must indicate for each question if your control
chart is exactly the same as seen in Teach One or other formats.
For each question, you must indicate if the answers you have provided is
the same as the answers supplied on the web. If there are no
answers provided, you must indicate that there were no answers available
on the web to compare your answers to.

Question 1: Construct a simple Multi-Morbidity Index. Assess the average severity of CHF, MI, Diabetes, Hypertension,
Alcohol Use, and ACL surgery (assume that sicker patients have
longer stays). To calculate the average severity associated with a
disease, compare all cases with the disease to all control patients without the
disease. Make sure that in each comparison, patients with and without disease
have the same set of comorbidities. For example, to find the average length of
stay for patients with MI, select all MI patients with the following
comorbidities: CHF,DM,AA. Then compare these cases to controls who do not
have MI but have the same comorbidities. To help you understand this assignment,
consider the following table. In this table, we see different strata of
mutually exclusive and exhaustive comorbidities. Then, within each strata
we can observe the impact of MI. The impact of MI is the average impact of
MI within each strata. Your objective is to create this table before you
calculate the impact of MI. To do so, first estimate n1 through n5 through
a file where the data is restricted to cases with MI (WHERE MI=1). Then,
estimate the values n6 through n10 through a file where the data are restricted
to non-MI patients (WHERE MI=0). Merge these data, making sure that you
match on the strata. Then you can calculate the impact of MI.
Data►Video►Slides►SQL Code►Answer►

Strata

Cases of MI

Controls with
No MI

Difference

AA

n1

n6

n1-n6

AA, CHF

n2

n7

n2-n7

AA,DM

n3

n8

n3-n8

DM, CHF

n4

n9

n4-n9

AA,DM,CHF

n5

n10

n5-n10

Question 2: In question 1, calculate the
likelihood ratio associated with each diagnosis in predicting above or below
average length of stay. First calculate the
weighted average length of stay for
each diagnosis. Assign individuals who have above weighted average length of
stay 1 and those below 0. Next calculate the
likelihood ratio (remembering to weight each strata by the count of patients
that fall into each strata). To calculate the likelihood ratio, select all
individuals who have above weighted average length of stay. Examine the
prevalence of the diagnosis among them. Select all individuals who
have below weighted average length of stay and select the prevalence of the diagnosis
among them. The ratio of these two calculated numbers constitutes the
likelihood ratio. Enter the likelihood ratios calculated for each
diagnosis into a Table called #LR. Data►SQL►Kanfer's Teach One►

ICD9

Likelihood Ratio

AA

CHF

DM

MI

3.47

Question 3: Using the Table #LR (see question 2),
calculate the probability of long stay for the 3 patients described below.
Please use SQL code to calculate multiplication of the likelihood ratios.
Note that diagnosis codes that do not have an associated likelihood ratio
should be assigned a likelihood ratio of 1 or ignored.

Question 4: Calculate the average age and the likelihood ratio associated
with diagnoses. You would need
to use SQL to do this assignment. You can use any SQL software,
including Access. Since the data is massive (17 million rows), keep in mind
that Access requires you to analyze the data in partitions. Microsoft SQL
server can analyze the entire data in one run. Submit your SQL code and the
10 diagnosis with highest and lowest likelihood ratios. For password to
access data contact your instructor. By
opening this file you agree not to share the file with anyone else.
Massive Data►Access►SQL Code► Marla's Guide► Answer►Manalac's Teach One►

Question 5: Calculate the Receiver Operating
Characteristic curve
associated with predicting from age whether the patient will
live or die. The data do not provide a predicted values, but you can
construct a prediction based on age exceeding the cutoff value. If the patient's
age exceeds the cutoff value, then we would predict
that the patient will die. For example, the cutoff values for the ages
provided can be 40, 50, 60, 70, 80, 90. If we take the cutoff value 60,
then we predict that all patients above 60 will die and all patients below 60
will live. Calculate sensitivity and specificity for all cutoffs. Then, draw the
Receiver Operating Characteristic curve. At what
age cutoff the sum of the
sensitivity and specificity is at its maximum?
Data►

Mid-Point of
Age Range

Total

45

55

65

75

85

True Condition

Alive

33

30

35

25

20

143

Dead

3

5

7

15

33

63

Total

36

35

42

40

53

206

Question 6: Calculate the Receiver Operating Characteristic curve for male and female ages
in the attached data set. The field "Alive" contains whether the patient
was alive in 6 months post assessment date. Alive is shown with 0
and dead with 1. Age at start of data collection is provided in the field
"Age." Patients' age at assessment must be calculated from age at start
and additional days till assessment. The field "DaysFirst" indicates number of
days since recording of the age. The field "Sex" indicates whether male
(M) or female (F). Data►SQL►Answer►

Question 7: Using the likelihood ratios provided, calculate the
probability of mortality for the cases described in the case file. What are
the IDs of the patients with highest probability of mortality.
Likelihood Ratios►Case files►